Transforming embedded systems education: The potential of large language models

Abstract

This conceptual article delves into the potential benefits, challenges, and future directions of how educators might adapt practices to accommodate the use of AI tools, using Large Language Models (LLMs) looking at embedded systems education as a case study. Drawing on literature pertaining to embedded systems education and the associated challenges, a new way of approaching embedded systems education is suggested, where students and LLMs are co-creators, working together to solve a problem. This article proposes that AI technologies have the potential to improve the productivity of students as they learn to program and that LLMs can be leveraged as personal tutors, facilitating adaptive tuition. The role of educators remains crucial in this process as students still require scaffolding and guidance on prompting LLMs. This article suggests that educators have different options when considering how to teach embedded systems with LLMs present, by changing the emphasis of teaching to focus on the process of learning and understanding and using constructive alignment of learning activities and assessment with the new goals. This promises to be an exciting avenue of research going forward.